Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Tags:
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - expert-generated | |
| language: | |
| - en | |
| license: | |
| - apache-2.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - extended|other-MS^2 | |
| - extended|other-Cochrane | |
| task_categories: | |
| - summarization | |
| - text2text-generation | |
| paperswithcode_id: multi-document-summarization | |
| pretty_name: MSLR Shared Task | |
| This is a copy of the [Cochrane](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used: | |
| - __query__: The `target` field of each example | |
| - __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`. | |
| - __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings | |
| - __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example | |
| Retrieval results on the `train` set: | |
| | Recall@100 | Rprec | Precision@k | Recall@k | | |
| | ----------- | ----------- | ----------- | ----------- | | |
| | 0.7014 | 0.3841 | 0.3841 | 0.3841 | | |
| Retrieval results on the `validation` set: | |
| | Recall@100 | Rprec | Precision@k | Recall@k | | |
| | ----------- | ----------- | ----------- | ----------- | | |
| | 0.7226 | 0.4023 | 0.4023 | 0.4023 | | |
| Retrieval results on the `test` set: | |
| N/A. Test set is blind so we do not have any queries. |